Abstract

The orally available novel small molecules PF06463922 [(10R)-7-amino-12-fluoro-2,10,16-trimethyl-15-oxo-10,15,16,17-tetrahydro-2H-8,4-(metheno)pyrazolo[4,3-h][2,5,11]benzoxadiazacyclotetradecine-3-carbonitrile] and PF06471402 [(10R)-7-amino-12-fluoro-2,10,16-trimethyl-15-oxo-10,15,16,17-tetrahydro-2H-8,4-(azeno)pyrazolo[4,3-h][2,5,11]benzoxadiazacyclo-tetradecine-3-carbonitrile] are second-generation anaplastic lymphoma kinase (ALK) inhibitors targeted to both naïve and resistant patients with non–small cell lung cancer (NSCLC) to the first-generation ALK inhibitor, crizotinib. The objectives of the present study were to characterize and compare the pharmacokinetic-pharmacodynamic (PKPD) relationships of PF06463922 and PF06471402 for target modulation in tumor and antitumor efficacy in athymic mice implanted with H3122 NSCLC cells expressing a crizotinib-resistant echinoderm microtubule-associated protein-like 4 (EML4)-ALK mutation, EML4-ALKL1196M. Furthermore, the PKPD relationships for these ALK inhibitors were evaluated and compared between oral administration and subcutaneous constant infusion (i.e., between different pharmacokinetic [PK] profiles). Oral and subcutaneous PK profiles of these ALK inhibitors were adequately described by a one-compartment PK model. An indirect response model extended with a modulator fit the time courses of PF06463922- and PF06471402-mediated target modulation (i.e., ALK phosphorylation) with an estimated unbound EC50,in vivo of 36 and 20 nM, respectively, for oral administration, and 100 and 69 nM, respectively, for subcutaneous infusion. A drug-disease model based on the turnover concept fit tumor growth curves inhibited by PF06463922 and PF06471402 with estimated unbound tumor stasis concentrations of 51 and 27 nM, respectively, for oral administration, and 116 and 70 nM, respectively, for subcutaneous infusion. Thus, the EC50,in vivo to EC60,in vivo estimates for ALK inhibition corresponded to the concentrations required tumor stasis in all cases, suggesting that the pharmacodynamic relationships of target modulation to antitumor efficacy were consistent among the ALK inhibitors, even when the PK profiles with different administration routes were considerably different.

Other potent small-molecule ALK inhibitors have also been identified along with PF06463922 in a drug-discovery program (Johnson et al., 2014). Among these, PF06471402 [(10R)-7-amino-12-fluoro-2,10,16-trimethyl-15-oxo-10,15,16,17-tetrahydro-2H-8,4-(azeno)pyrazolo[4,3-h][2,5,11]benzoxadiazacyclo-tetradecine-3-carbonitrile] is a structurally similar analog of PF06463922, differing in a core group between pyrazine and pyridine (Fig. 1), while having different pharmacokinetic (PK) and pharmacodynamic (PD) characteristics. The EC50,in vitro for PF06471402 against the wild-type EML4-ALK (∼1 nM) and mutant EML4-ALKL1196M (∼6 nM) were ∼3-fold lower than those for PF06463922 (Johnson et al., 2014). In vivo estimates for clearance and volume of distribution were 2- to 3-fold higher for PF06471402 relative to PF06463922 in nonclinical species. These novel ALK inhibitors can be positioned as interesting tool compounds to investigate mechanistic aspects of quantitative PKPD relationships. Given that crizotinib and PF06463922 were orally administered to nonclinical models (Yamazaki et al., 2008, 2012, 2014; Yamazaki, 2013), the impact of different PK profiles on the PKPD relationships (e.g., oral administration, clinical administration route, versus constant infusion) has not been investigated yet. The comparison of PKPD relationship arising from different PK profiles can be valuable for translational pharmacology because the PK profiles are generally different (in some cases, substantially different) between nonclinical models and patients. The objectives of the present study were to quantitatively characterize and compare how the systemic exposures of PF06463922 and PF06471402 related to inhibition of ALK phosphorylation in tumor and tumor growth inhibition in athymic mice implanted with H3122 NSCLC cells expressing crizotinib-resistant EML4-ALKL1196M. Furthermore, these mechanistic PKPD relationships were compared when the PK profiles were different (i.e., oral administration versus subcutaneous constant infusion).

Materials and Methods

Chemicals

PF06463922 and PF06471402 were synthesized by Pfizer Worldwide Research and Development (San Diego, CA) (Johnson et al., 2014). All other reagents and solvents were commercially available and were of either analytical or high-performance liquid chromatography grade.

In Vivo PKPD Study

The experimental designs and methods of the in vitro and in vivo PKPD studies were previously reported in part (Zou et al., 2013; Yamazaki et al., 2014). Briefly, four separate PKPD studies were conducted with PF06463922 and PF06471402 in female athymic nu/nu mice implanted with subcutaneous xenografts of H3122 NSCLC cells expressing EML4-ALKL1196M (Table 1). For simplicity, the NSCLC xenograft model with EML4-ALKL1196M is henceforth referred to as an ALK-xenograft model. PF06463922 and PF06471402 were administered to animals orally twice daily, 7 hours apart (studies 1 and 3, respectively) or subcutaneously via ALZET osmotic pumps (Durect Co., Cupertino, CA) at the constant infusion rate of 0.5 μl/h (studies 2 and 4, respectively). Oral doses of PF06463922 and PF06471402 in acidified water were 0.6, 2, 6, 20, and 40 mg/kg per day twice daily up to 13 days (study 1) and 1.2, 4, 12, 40, and 60 mg/kg per day twice daily for 11 days (study 3), respectively. Daily subcutaneous infusion doses of PF06463922 and PF06471402 in the solution of dimethylsulfoxide, PEG400, and Cremophor ELP (45:40:15, v/v) were 0.5, 1.5, 5, 15, and 40 mg/kg per day for 13 days (study 2) and 0.5, 1.5, 5, 15, and 50 mg/kg per day for 12 days (study 4), respectively. The results from the repeated oral-dose studies of PF06463922 have been reported recently (Yamazaki et al., 2014): two separate repeated oral-dose PKPD studies in the previous report (i.e., 4- and 13-day repeated oral-dose studies) were combined in study 1 here. The intent of the previously reported results was to provide a head-to-head comparison between PF06463922 versus PF06471402. Animals were randomized into six groups on the first dosing day of each study. On the last dosing day, a subset of mice (n = 3/time point) was humanely euthanized at 1, 3, 7, 8, and 24 hours after the first daily dose in studies 1 and 3 or at approximate same time as the ALZET pump implantation in studies 2 and 4 to collect blood and tumor samples. Blood samples were collected by exsanguinations via cardiac puncture to determine plasma concentrations of PF06463922 and PF06471402. Blood samples were also collected from the tail vein on days 1, 3, and 7 during the ALZET pump infusion (studies 2 and 4). In addition, a subset of mice (n = 3/time point) was humanely euthanized on day 7 in study 4 to collect blood and tumor samples. Resected tumors were snap-frozen and pulverized using liquid nitrogen–cooled cryomortar. Protein lysates were generated, and the level of total phosphorylated ALK protein (ALK phosphorylation) was determined using a capture enzyme-linked immunosorbent assay (ELISA) method by using the PathScan Phospho-ALK (Tyr1604) Chemiluminescent Sandwich ELISA Kit (Cell Signaling Technology, Danvers, MA) and PathScan Total ALK Chemiluminescent Sandwich ELISA Kit (Cell Signaling Technology) according to the manufacturer’s protocol. The back-calculated calibration standard concentrations were within 15% of their theoretical concentrations with a few exceptions. Tumor volume was measured during the treatment period by electronic Vernier calipers and was calculated as the product of its length × width2 × 0.4. Tumor growth inhibition (%) in each PF06463922 and PF06471402 treatment group was calculated as 100 × (1 − ∆T/∆C), where ∆T and ∆C are the differences in the median tumor volumes between the first and last dosing days in the treatment and vehicle control groups, respectively. When the calculated percentage of tumor growth inhibition was greater than 100%, tumor regression (%) was also calculated as 100 × (∆T/Tinitial), where Tinitial is the median tumor volume on the first dosing day. All procedures were conducted in accordance with the Institute for Laboratory Animal Research Guide for the Care and Use of Laboratory Animals and with Pfizer Animal Care and Use Committee guidelines.

Analyses of PF06463922 and PF06471402 in Plasma

Plasma concentrations of PF06463922 and PF06471402 were quantitatively determined by a liquid-chromatography tandem mass spectrometry method after protein precipitation of plasma samples. This system consisted of Waters Acquity UPLC system (Waters, Milford, MA) and an API 5500 triple-stage quadrupole mass spectrometer (Applied Biosystems, Foster City, CA). Both instruments were controlled by Analyst 1.5.2 software (Applied Biosystems). Chromatographic separation of the analytes was achieved using a reverse phase column (Phenomenex Kinetex phenyl-hexyl, 50 × 2 mm 1.7 µm) at a flow rate of 0.5 ml/min. A binary mobile phase consisted of water with 0.1% formic acid (A) and acetonitrile with 0.1% formic acid (B). The gradient started at 5% B for 0.2 minutes, increased to 95% B over 1.3 minutes, and then held at 95% B for 0.5 minutes. The gradient was returned to the initial condition of 5% B in 0.1 minutes and equilibrated at 5% B for 0.5 minutes before the next injection. The mass spectrometer was operated in the positive ionization mode using multiple-reaction monitoring at specific precursor ion → product ion transition, m/z 407.3→228.0 for PF06463922, m/z 408.2→229.0 for PF06471402, and m/z 472.3→432.6 for the internal standard (terfenadine). The standard calibration curve was constructed using weighted (1/×2) linear regression. The calibration curve range was 0.5–5000 ng/ml. The back-calculated calibration standard concentrations were within 15% of their theoretical concentrations, with coefficients of variation of less than 15%. The precision and accuracy of the quality control samples were within 15%.

Pharmacokinetic Data Analysis

A naïve-pooled PK analysis was used to estimate PK parameters of PF06463922 and PF06471402 in ALK-xenograft models. Since full plasma concentration-time profiles in each animal were not available, all individual data at each dose were pooled together for PK analysis as if they came from a single animal (Beal and Sheiner, 1992). PK analysis was performed with a standard one-compartment model as implemented in NONMEM version 7.1.2 (University of California at San Francisco, San Francisco, CA) (Sheiner et al., 1979). The one-compartment PK model (subroutine ADVAN2 with TRANS2) was parameterized using absorption rate constant (ka, h−1), oral clearance (CL/F, liters per h/kg), and oral volume of distribution (V/F, l/kg). The PK parameters were estimated together at all doses of each study since plasma concentrations of PF06463922 and PF06471402 roughly increased in proportion to the doses tested in all studies. Residual variability was characterized by a proportional error model.

PKPD Modeling

Target Modulation.

The ALK responses in the treatment group were expressed as the ratios to vehicle control animal data, meaning that the ratios of 1 and 0 represent 0 and 100% inhibition, respectively. In oral-dose studies 1 and 3, the ALK rebounds were observed at 24 hours after the dose (i.e., ALK phosphorylation ratios greater than unity, i.e., above baseline, in the treatment groups relative to the control group). As previously reported (Yamazaki et al., 2014), the inhibition of ALK phosphorylation in tumor to the plasma concentrations of ALK inhibitors was therefore modeled by an indirect response model with a hypothetical modulator (i.e., precursor model) to take account of the observed ALK rebounds (Jusko and Ko, 1994; Sharma et al., 1998).

The precursor model assumed that a modulator (M) was synthesized at a zero-order rate (kin) and degraded at a first-order rate (kmd); ALK phosphorylation was maintained by a balance of the first-order formation rate provided by the modulator degradation rate (kmd) and the ALK degradation rate (kout). Accordingly, the following differential equations (eqs. 1 and 2) were used to estimate the EC50 required for ALK inhibition:(1)(2)where Cp is the plasma concentration of ALK inhibitors (ng/ml), Emax is maximal effect, EC50 is the plasma concentration of ALK inhibitors (ng/ml) causing one-half Emax, kin is the zero-order formation rate constant (h−1), kmd is the first-order formation rate for ALK phosphorylation provided by the modulator degradation rate (h−1), kout is the first-order degradation rate constant (h−1) for ALK phosphorylation, and γ is the Hill coefficient.

Antitumor Efficacy.

To perform drug-disease modeling, antitumor efficacy to plasma concentration of ALK inhibitors was modeled by a modified indirect response model as reported previously (Yamazaki et al., 2014). Briefly, the individual tumor growth curves in the vehicle control group were first modeled by using a first-order net growth rate without (exponential growth) and with saturation at the maximal sustainable tumor volume (logistic growth). Maximal sustainable tumor volume was assumed to be constant, whereas tumor volume changed over time. The exponential and logistic tumor growth models are as follows in eqs. 3 and 4:(3)(4)where kng, T, and Tss represent the first-order net growth rate constant (h−1), tumor volume (mm3), and maximum sustainable tumor volume (mm3), respectively.

The logistic tumor growth model is applicable if the growth rate starts to slow down in the later stage of tumor growth. The model implies that the net tumor growth rate is roughly first-order (i.e., exponential growth) when T is relatively small, e.g., during the early stage of tumor growth, since the ratio of T/Tss approximates near zero. Thereafter, the net tumor growth rate eventually becomes zero when the T/Tss ratio approaches unity. The logistic model was used in all studies since the model provided better fits to the individual tumor growth curves of the vehicle control groups compared with the exponential model.

Subsequently, the response of tumor volume (T) to plasma concentration of ALK inhibitors (Cp) was modeled based on the assumption that ALK inhibitors stimulated the tumor killing rate, thus inhibiting the tumor growth rate:(5)where g(T) is the tumor growth function characterized in the vehicle control group, Cp is the plasma concentration of ALK inhibitors (ng/ml), Kmax is the maximal tumor killing rate constant (h−1) caused by the ALK inhibitors, KC50 is the plasma concentration of ALK inhibitors (ng/ml) corresponding to one-half Kmax, and γ is the Hill coefficient.

Data Analysis.

All PKPD modeling analyses were performed with NONMEM version 7.1.2 with the subroutine ADVAN8. The initial conditions at time zero for the gastrointestinal tract compartment, ALK phosphorylation ratio, and tumor volume were the dose amount (mg/kg), the ALK baseline ratio (i.e., unity), and the measured initial individual tumor volume (mm3), respectively. Residual variability was characterized by a proportional error model. In the drug-disease model, an interanimal variability on kng was estimated by mixed-effect modeling using an exponential variance model. As is customarily done, model selection was based on a number of criteria such as the NONMEM objective function values (OFVs), estimates, their standard errors, overall biologic and scientific plausibility, and exploratory analysis of standard goodness-of-fit plots. The difference in the OFVs of two nested models was compared with a χ2 distribution in which a difference of 6.63 was considered significant at the 1% level (Wahlby et al., 2001).

Results

PK of PF06463922 and PF06471402.

The observed and one-compartment model-fitted plasma concentrations of PF06463922 (studies 1 and 2) and PF06471402 (studies 3 and 4) are presented in Fig. 2. Overall, the plasma concentration-time profiles of PF06463922 and PF06471402 in each study were adequately described by the one-compartment model. PK parameter estimates in each study are summarized in Table 2. The apparent CL/F estimates for PF06463922 (0.84–1.1 liter per h/kg) were lower than those for PF06471402 (1.7–3.3 liters per h/kg); the apparent V/F estimates for PF06463922 (7.0–13 l/kg) were smaller than those for PF06471402 (14–32 l/kg). The ka estimates for PF06463922 and PF06471402 were larger for oral administration (1.3 and 7.6 h−1, respectively) than subcutaneous infusion (0.062 and 0.036 h−1, respectively). The standard errors of the PK parameter estimates in all studies were small (CV < 30%). The typical PK parameters thus obtained were used to simulate plasma concentrations as a function of time after oral administration and subcutaneous infusion to drive the PD models for the PKPD analysis.

One-compartment model-fitted and observed plasma concentrations of PF06463922 (S1 and S2) and PF06471402 (S3 and S4) in athymic mice implanted with H3122 NSCLC cells expressing EML4-ALKL1196M after repeated oral administration (A) or subcutaneous infusion (B). Animals received twice-daily oral doses of PF06463922 at 0.6, 2, 6, 20, and 40 mg/kg daily for 13 days in study 1 (S1), subcutaneous infusion doses of PF06463922 at 0.5, 1.5, 5, 15, and 40 mg/kg daily for 13 days in study 2 (S2), twice-daily oral doses of PF06471402 at 1.2, 4, 12, 40, and 60 mg/kg daily for 11 days in study 3 (S3), and subcutaneous infusion doses of PF06471402 at 0.5, 1.5, 5, 15, and 50 mg/kg daily for 12 days in study 4 (S4). The x-axis represents the time after dosing on the last dosing day in hours (A) or the subcutaneous infusion period in days (B) and the y-axis represents the observed plasma concentrations of PF06463922 and PF06471402 (OBS) with the model-fitted individual (IPRED) and typical (PRED) profiles in nanograms per milliliter on a logarithmic scale.

Precision of the estimates is expressed as S.E. in parentheses. PF06463922 and PF06471402 were administered to animals orally twice daily, 7 hours apart (studies 1 and 3) or continuously via subcutaneous infusion with ALZET osmotic pumps (studies 2 and 4).

PKPD Modeling for Target Modulation.

ALK phosphorylation was dose-dependently inhibited by PF06463922 and PF06471402 in oral-dose studies 1 and 3, respectively, whereas the ALK responses in all groups returned to near or above baseline at 24 hours postdose. ALK phosphorylation was also dose-dependently inhibited by PF06463922 and PF06471402 during subcutaneous infusion in studies 2 and 4, respectively. The observed and precursor model-fitted ALK phosphorylation time courses in studies 1–4 are shown in Fig. 3. The precursor model reasonably fit both PF06463922- and PF06471402-mediated ALK inhibition, including the recovery phase of ALK responses after oral administration in studies 1 and 3, respectively. The precursor model was also able to fit PF06463922- and PF06471402-mediated ALK inhibition in subcutaneous infusion studies 2 and 4, respectively. The EC50,in vivo values of PF06463922 and PF06471402 in studies 1, 2, 3, and 4 were estimated to be 58, 162, 40, and 140 ng/ml, respectively (Table 3).

Precursor model-fitted and observed ALK inhibition by PF06463922 (S1 and S2) and PF06471402 (S3 and S4) in athymic mice implanted with H3122 NSCLC cells expressing EML4-ALKL1196M after repeated oral administration (A) or subcutaneous infusion (B). Animals received twice-daily oral doses of PF06463922 at 0.6, 2, 6, 20, and 40 mg/kg daily for 13 days in study 1 (S1), subcutaneous infusion doses of PF06463922 at 0.5, 1.5, 5, 15, and 40 mg/kg daily for 13 days in study 2 (S2), twice-daily oral doses of PF06471402 at 1.2, 4, 12, 40, and 60 mg/kg daily for 11 days in study 3 (S3), and subcutaneous infusion doses of PF06471402 at 0.5, 1.5, 5, 15, and 50 mg/kg daily for 12 days in study 4 (S4). The x-axis represents the time after dosing on the last dosing day in hours (A) or the subcutaneous infusion period in days (B). The left side of the y-axis represents the observed (ALK OBS) and model-fitted (ALK PRED) ALK inhibition in the ratio to the mean value of control animal data, and the right side of y-axis represents the model-predicted plasma concentrations of PF06463922 and PF06471402 (CP PRED) in nanograms per milliliter on a logarithmic scale.

Precision of the estimates is expressed as S.E. in parentheses. Emax was fixed at unity in all studies. PF06463922 and PF06471402 were administered to animals orally twice daily, 7 hours apart (studies 1 and 3) or continuously via subcutaneous infusion with ALZET osmotic pumps (studies 2 and 4).

Precision of the estimates is expressed as S.E. in parentheses. Hill coefficient (γ) was fixed at unity in all studies. PF06463922 and PF06471402 were administered to animals orally twice daily, 7 hours apart (studies 1 and 3) or continuously via subcutaneous infusion with ALZET osmotic pumps (studies 2 and 4).

Quantitative Comparison of PKPD Relationships.

Plasma concentration-response curves of PF06463922 and PF06471402 for ALK and tumor growth inhibition based on the PD parameters (e.g., EC50,in vivo and Emax) obtained from ALK-xenograft models are graphically presented in Fig. 5. Since the model-predicted maximal antitumor efficacy by both PF06463922 and PF06471402 was tumor regression (i.e., > 100% tumor growth inhibition), the tumor growth inhibition on the y-axis of Fig. 5 ranges from 0–120%, whereas the range of ALK inhibition is 0–100%. In all studies, the plasma concentration-ALK response curves for PF06463922 and PF06471402 are shifted to the right compared with the tumor growth inhibition curves. The efficacious concentrations of PF06463922 and PF06471402, summarized as EC50,in vitro, EC50,in vivo and EC60,in vivo for target modulation and Tsc for antitumor efficacy, are shown in Table 5. These concentrations in vivo were converted from total (bound plus unbound) to unbound concentrations using the unbound fraction in mouse plasma (0.25 and 0.20 for PF06463922 and PF06471402, respectively). In oral-dose studies 1 and 3, the EC50,in vivo to EC60,in vivo (EC50–60,in vivo) estimates for ALK inhibition (i.e., 36 to 52 nM free and 20 to 32 nM free, respectively) were comparable to the Tsc (51 and 27 nM free, respectively). Similarly, the EC50–60,in vivo estimates for ALK inhibition in subcutaneous infusion studies 2 and 4 (i.e., 100–119 nM free and 69–96 nM free, respectively) were comparable to the Tsc (116 and 70 nM free, respectively). Thus, the EC50–60,in vivo estimates for ALK inhibition consistently corresponded to the Tsc estimates in all studies, despite the differences in the inhibitors (i.e., PF06463922 and PF06471402) and their PK profiles (i.e., oral administration and subcutaneous infusion).

Discussion

Using a quantitative mathematical modeling approach, the present study with the ALK inhibitors, PF06463922 and PF06471402, demonstrated a consistent in vivo PD relationship of target modulation (inhibition of ALK phosphorylation) to antitumor efficacy (tumor growth inhibition) as summarized in Table 5 and Fig. 5. That is, the EC50–60,in vivo estimates for both PF06463922- and PF06471402-mediated ALK inhibition consistently corresponded to the concentrations required for 100% tumor growth inhibition (estimated as Tsc). Furthermore, the PD relationships of these ALK inhibitors were also consistent between the different PK profiles (i.e., oral administration and subcutaneous constant infusion). Collectively, these findings suggested that ∼60% ALK inhibition would be required for tumor stasis in ALK-xenograft models for any ALK inhibitors with any PK profiles.

To characterize the inhibition of ALK phosphorylation, an indirect response model with a precursor was applied here based on a previous report (Yamazaki et al., 2014). The indirect response model is based on the turnover concept required to maintain ALK phosphorylation constant by a balance of formation and degradation rates. In addition, a hypothetical modulator was incorporated as a precursor to take account of the observed ALK rebounds (Sharma et al., 1998). The OFVs provided by the precursor models were smaller than those by the indirect response models (without a modulator) in all studies (Supplemental Table 1), indicating improved model performance. As shown in Supplemental Fig. 1, the indirect response model was not able to account for the ALK rebound, particularly at 24 hours postdose, in oral-dose studies 1 and 2. The indirect response model also tended to underpredict ALK inhibition on day 7 in subcutaneous infusion study 4. Since the biologic mechanism for ALK rebounds in an ALK-tumor model still remains unclear, several other feedback and pool models were applied to the present results (Gabrielsson and Weiner, 2000). None of these PKPD models except the precursor model could acceptably fit the time course of ALK responses in the present oral-dose studies.

To characterize tumor growth inhibition by ALK inhibitors, the drug-disease (tumor growth inhibition) model used in the present study was also based on the turnover concept. The model assumed that ALK inhibitors stimulated a tumor killing rate, in turn inhibiting a net tumor growth rate (kng), supposedly maintained by a balance of formation and degradation rates. Thus, when an estimated maximal tumor killing rate (Kmax) was larger than kng, a model-predicted maximal antitumor efficacy was greater than 100% tumor growth inhibition (i.e., tumor regression ensued). In all studies, the Kmax estimates were 1.1- to 1.5-fold larger than the kng estimates (Table 4), suggesting that the model-predicted maximal antitumor efficacy of both PF06463922 and PF06471402 was tumor regression, as suggested by the observed results. In a previous report (Yamazaki et al., 2012), the observed maximal antitumor efficacy by the first-generation ALK inhibitor, crizotinib, was near tumor stasis in an H3122 wild-type xenograft model (without mutations) at the highest dose tested, 200 mg/kg, resulting in tumor stasis being the model-predicted maximal efficacy. Moreover, crizotinib failed to exhibit significant antitumor efficacy in an ALK-xenograft model with crizotinib-resistant mutant EML4-ALKL1196M at twice-daily oral doses of 150 mg/kg daily, which yielded unbound plasma concentrations in mice higher than in patients at the clinically recommended twice-daily oral doses of 250 mg (Zou et al., 2013). The second-generation ALK inhibitors, PF06463922 and PF06471402, attained tumor regressions in the ALK-xenograft model with EML4-ALKL1196M. Therefore, we could expect that the second-generation ALK inhibitors would potentially have a better antitumor efficacy than crizotinib in the clinic, provided their unbound exposures reached efficacious levels.

When comparing in vivo potencies of the second-generation ALK inhibitors between oral administration and subcutaneous infusion, the EC50,in vivo estimates for PF06463922 and PF06471402 were 3- to 4-fold lower in oral-dose studies (36 and 20 nM, respectively) than in subcutaneous infusion studies (100 and 69 nM, respectively). These EC50,in vivo estimates were 2- to 15-fold greater than the corresponding EC50,in vitro values (Table 5). Establishing an in vitro–in vivo correlation of ALK inhibition potency would thus appear to be difficult since the EC50,in vivo estimates depended on the PK profiles after different administration routes. The Tsc estimates for PF06463922 and PF06471402 in oral-dose studies (51 and 27 nM, respectively) were also lower than those estimated in subcutaneous infusion studies (116 and 70 nM, respectively). Thus, different ALK inhibitors with different PK profiles yielded different PKPD relationships from their PK profiles to target modulation (inhibition of ALK phosphorylation), as shown by the different EC50,in vivo estimates for ALK inhibition. These differences in EC50,in vivo estimates could ultimately result in the different Tsc between the different ALK inhibitors and between the different PK profiles. The reason oral administration was more effective on target modulation and antitumor efficacy than subcutaneous infusion remains unclear. One of the potential reasons could be due to the biologic signaling mechanism and the degree of network feedback, as network signals can quickly undergo potential adaptive changes (Riely et al., 2007; Soria et al., 2012; Rosell et al., 2013). In the present study, significant ALK inhibition (e.g., near 100% inhibition at the higher doses) was clearly achieved immediately after oral administration of PF06463922 and PF06471402, even though ALK rebounds were observed before each dose. To inhibit ALK phosphorylation significantly and effectively, ultimately leading to more robust antitumor efficacy, the maximal ALK inhibition corresponding to the maximal plasma concentrations after oral administration might be more relevant than the constant ALK inhibition achieved by maintaining steady-state plasma concentrations during subcutaneous infusion. Despite ALK responses having returned to near or above baseline before each dose, a clear dose-dependent tumor growth inhibition by PF06463922 and PF06471402 was consistently observed throughout the oral-dose treatment periods. A dose-dependent tumor growth inhibition by both inhibitors was also observed during subcutaneous infusion periods. These findings suggested that the effect of ALK rebounds on antitumor efficacy could be negligible in ALK-xenograft models.

To summarize, the PKPD relationships of systemic exposures of PF06463922 and PF06471402 to PD biomarker (i.e., inhibition of ALK phosphorylation in tumor) and pharmacologic efficacy (i.e., tumor growth inhibition) in ALK-xenograft models were characterized in a quantitative manner using a mathematical modeling approach (Fig. 6). The present study demonstrated that the EC50–60,in vivo estimates for PF06463922- and PF06471402-mediated ALK inhibition consistently corresponded to the estimated Tsc in all studies, suggesting that the mechanistic PD relationships could ultimately be consistent not only between different ALK inhibitors but also between different PK profiles. Moreover, these findings suggest that any differences in the characteristics of ALK inhibitors, such as physicochemical property, ALK inhibition potency, and PK profiles, could have negligible effects on the PD relationships of target modulation to antitumor efficacy. Thus, a given target’s PKPD relationship could possibly be translatable from nonclinical models to patients and also from patients to patients, even if PK profiles were considerably different between nonclinical models and patients or among patients. Based on the PKPD results in oral-dose studies, we have previously proposed that the EC60,in vivo estimates for ALK inhibition could be considered a minimal target efficacious concentration of PF06463922 in cancer patients with EML4-ALK rearrangements (Yamazaki et al., 2014). That being said, given the extent of target modulation that differed between PK profiles, a study with one administration route might be an overly simplistic approach to project a target efficacious concentration of molecularly targeted agents (e.g., EC60,in vivo for ALK inhibition corresponding to Tsc). As we remarked earlier, the PK profiles could influence a degree of target modulation, resulting in the different EC50,in vivo estimates; therefore, accounting for these effects on PKPD relationships via a quantitative dynamic modeling approach would be central to properly understand the PKPD relationships relevant to translational pharmacology.

Ideally, clinical PKPD relationships of antitumor agents’ exposures to target modulation (or its surrogate biomarker response) could be established in phase I trials, for example, in an expanded cohort with selected patients. However, it is generally difficult to obtain tumor biopsy samples from patients, especially serial samples of individual tumors to measure its time courses. In fact, only 20% of ∼2500 phase I trials submitted to the American Society of Clinical Oncology incorporated biomarker assessments (Goulart et al., 2007). An alternative course of action would be a modeling approach to predict time courses of target modulation in relation to the observed degree of antitumor efficacy in a patient population (based on nonclinical PKPD modeling results) as soon as PK profiles are available in the clinic. A clinically recommended dose for molecularly targeted agents should be established as a pharmacologically active dose based on an expected degree of target modulation in tumor (or surrogate biomarker response) rather than a maximal tolerated dose based on dose-limiting toxicity as traditionally conducted for cytotoxic agents. The pharmacologically active dose approach has been relatively rare in the oncology field, but it has potential to maximize benefits for cancer patients and minimize possible risks of molecularly targeted agents (Plummer et al., 2008; Le Tourneau et al., 2009; Stroh et al., 2014). This approach would also be valuable to conducting phase I trials of subsequent drug candidates (e.g., second-generation inhibitors) safely and effectively. We believe that the consistent PD relationships demonstrated in the present study could contribute, in part, to making this approach successful in the clinic. We also believe that quantitative PKPD understanding would be a key asset for translational pharmacology and could ultimately increase the success rate of molecularly targeted therapies in the clinic.

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